1. Field of the Invention
The present invention relates to pattern recognition systems. More specifically, the present invention relates to an acoustical pattern recognition system implemented as a neural network which performs real-time decomposition of input patterns into their primitives of energy, space (frequency) and time relations and then decodes these primitives into phonemes or diphones which are recognized parts of speech.
2. Description of the Prior Art
Pattern recognition has been accomplished in various ways in the prior art. One of the best known methods of pattern recognition is typified by a simple radar system wherein a beam of electromagnetic energy illuminates a target and is backscattered to a receiver set which is coupled to a computer that analyzes the backscattered signal and forms an image of the target. Similarly, sonar systems accomplish the same result with acoustical type signals.
Regardless of the transmission and receiving apparatus used in these systems, a multi-purpose, digital computer is continually utilized to perform complex calculations to obtain an output which identifies the input signal. The types of computers used in the prior art to perform such calculations have been exclusively sequential machines that require sophisticated programming to effectively perform pattern recognition algorithms such as Fourier transforms, fast Fourier transforms and similar types of algorithms known to those with ordinary skill in the art.
A major drawback which exists with the use of digital, sequential computers in pattern recognition systems is the inherent limitation of these computers to perform their function in a strictly sequential fashion. It is known that sequential, digital computers perform one step of a process or algorithm over each machine cycle. In this manner, successive iterations are repeated over a large number of computer machine cycles of a complex algorithm in order to perform a pattern recognition function.
Depending upon the complexity of the algorithm, the digital computer must perform enormous numbers of machine cycles to form the complete solution of a complex algorithm. For example, when higher order differential equations must be solved simultaneously or when a large number of differential equations must be solved either simultaneously or sequentially, the number of machine cycles required to solve the equations increases drastically. With these drastic increases in machine cycles comes an increased time period for the digital, sequential computer to perform a complete analysis of incoming data. Those skilled in the art will appreciate that complete and useful pattern recognition with such digital computers can take hours or even days. Thus, the use of digital computers generally does not allow pattern recognition in "real-time."
There is therefore a long felt need in the pattern recognition art for a machine which can drastically reduce the time required to achieve pattern recognition. Some form of parallel processing of incoming signals would perform this function, and the use of a parallel processor or a machine capable of inherent parallelism could allow pattern recognition of a complex signal in real-time.
An additional problem which has existed in the pattern recognition art arises from the requirement that signals be resolved into digital components before they may be processed by a sequential, digital computer. This requires that all incoming signals be first "digitized" by an "analog to digital" component of the pattern recognition system before the digital computer can begin processing the signal with its particular pattern recognition algorithm. This places many burdens on prior art pattern recognition systems in that it requires expensive hardware to implement analog to digital conversion and increases the overall processing time of such systems by requiring the analog to digital conversion step. Thus, a pattern recognition system which utilizes incoming analog signals directly without analog to digital conversion is highly desirable.
Neural networks which are patterned after the intricate and sophisticated neural system of the brain are viewed in accordance with the present invention as providing an ideal model by which speedy parallel processing of analog signals can be accomplished for accurate pattern recognition. As known to those skilled in the art, neural networks are electronic networks which mimic the behavior of brain neurons and are appropriately interconnected to provide a desired processing function. As used herein, the term "neuron" is used without distinction for real neurons--those found in the brain--and for artificial neurons--those made from electronic components. It has been previously established that it is possible to construct artificial neurons which are, as far as input-output relations are concerned, complete analogs of the biological counterpart. Such technology is applied in accordance with the present invention for performing rapid processing of analog signals to provide an efficient pattern recognition technique.
The present inventor has previously established in a paper entitled "General Principles of Operation in Neuron Nets With Application to Acoustical Pattern Recognition", Mueller et al., Biological Prototype and Synthetic Systems, Vol. I, pp. 192-212, Plenum, New York (1962), that neurons may be used as logical devices to construct networks which perform logical operations for providing methods of quantitative calculations of input-output relations. In particular, as described therein, a neuron sees the output of its neighbors only up to integration at its synapse so that, as far as the individual neuron is concerned, there exist no input pulses. Its input and output are instead voltages which continue through varying periods of time and which are, within limits, continuously variable. Input pulses to the neurons may be integrated by an RC circuit of the synapse, whereby output pulses may be integrated at the synapse of a succeeding neuron. The input-output characteristics, normally mostly logarithmetic, also can be varied by positive or negative feedback from linearity to a complete step function Neurons thus function essentially as nonlinear amplifiers having a lower and upper bound and its characteristics can be controlled by external connections through feedback.
The output of neurons within a neural network can have a certain voltage, have a certain duration or occur at a certain time, and have an extension in space (i.e., involve a number of neurons) or occur at a certain place. Thus, there are only three variables or domains on which the system can operate, namely, energy, time and space. The basic operations on these variables are those of addition and subtraction in space or time (integration) and "if--then" type operations which are the consequence of the transmission of energy from one neuron to the next. As noted in the above-mentioned article, the discontinuity (quantization) in space or in the input-output characteristics (threshold) make logical operations of the Boolean type possible.
Mueller et al. further demonstrated in the above-mentioned article that networks of such neurons may be developed for acoustical pattern recognition using a transistorized neuron model. Such a network preferably comprises low output and input impedances to minimize sneak paths through the logic nets so as to allow many connections to each neuron. Preferably, integration and refractory time constants that are independent of the number and strength of the input connections to a neuron are also used. In addition, such circuits are designed to have stable thresholds which vary less than one percent. An artificial neuron of this type is disclosed therein. Mueller et al. further demonstrate that such artificial neurons may be used for the initial transformation of the auditory pattern to neural signals using a simulated cochlea and artificial neural networks. A sample neural network for acoustical pattern recognition is described which recognizes simple vowels and some consonants through the use of excitatory and inhibitory connections necessary for logical stability.
However, although the Mueller et al. article illustrates that it has long been known by the present inventor that neural nets may be used for general acoustical pattern recognition, the system described therein generally functions only to break the input acoustical patterns into a limited number of primitives which may or may not be recognized as certain vowel and consonants sounds. A more generalized system is desired which allows the primitives to be related to known phonemes and diphones which are known elements of speech which can be readily recognized when displayed. The present invention has been designed to meet this long-felt need.